Search Results for author: Liang Zeng

Found 11 papers, 5 papers with code

MolKD: Distilling Cross-Modal Knowledge in Chemical Reactions for Molecular Property Prediction

no code implementations3 May 2023 Liang Zeng, Lanqing Li, Jian Li

This paper studies this problem and proposes to incorporate chemical domain knowledge, specifically related to chemical reactions, for learning effective molecular representations.

Drug Discovery Molecular Property Prediction +1

Copy-Pasting Coherent Depth Regions Improves Contrastive Learning for Urban-Scene Segmentation

1 code implementation25 Nov 2022 Liang Zeng, Attila Lengyel, Nergis Tömen, Jan van Gemert

For unsupervised semantic segmentation of urban scenes, our method surpasses the previous state-of-the-art baseline by +7. 14% in mIoU on Cityscapes and +6. 65% on KITTI.

Contrastive Learning Depth Estimation +2

ImGCL: Revisiting Graph Contrastive Learning on Imbalanced Node Classification

no code implementations23 May 2022 Liang Zeng, Lanqing Li, Ziqi Gao, Peilin Zhao, Jian Li

Motivated by this observation, we propose a principled GCL framework on Imbalanced node classification (ImGCL), which automatically and adaptively balances the representations learned from GCL without labels.

Classification Contrastive Learning +2

Spatio-Temporal meets Wavelet: Disentangled Traffic Flow Forecasting via Efficient Spectral Graph Attention Network

no code implementations6 Dec 2021 Yuchen Fang, Yanjun Qin, Haiyong Luo, Fang Zhao, Bingbing Xu, Chenxing Wang, Liang Zeng

Traffic forecasting is crucial for public safety and resource optimization, yet is very challenging due to three aspects: i) current existing works mostly exploit intricate temporal patterns (e. g., the short-term thunderstorm and long-term daily trends) within a single method, which fail to accurately capture spatio-temporal dependencies under different schemas; ii) the under-exploration of the graph positional encoding limit the extraction of spatial information in the commonly used full graph attention network; iii) the quadratic complexity of the full graph attention introduces heavy computational needs.

Graph Attention Time Series Analysis

CDGNet: A Cross-Time Dynamic Graph-based Deep Learning Model for Traffic Forecasting

no code implementations6 Dec 2021 Yuchen Fang, Yanjun Qin, Haiyong Luo, Fang Zhao, Liang Zeng, Bo Hui, Chenxing Wang

Besides, we propose a novel encoder-decoder architecture to incorporate the cross-time dynamic graph-based GCN for multi-step traffic forecasting.

Inductive Matrix Completion Using Graph Autoencoder

2 code implementations25 Aug 2021 Wei Shen, Chuheng Zhang, Yun Tian, Liang Zeng, Xiaonan He, Wanchun Dou, Xiaolong Xu

However, without node content (i. e., side information) for training, the user (or item) specific representation can not be learned in the inductive setting, that is, a model trained on one group of users (or items) cannot adapt to new users (or items).

Matrix Completion Recommendation Systems

Trade When Opportunity Comes: Price Movement Forecasting via Locality-Aware Attention and Iterative Refinement Labeling

no code implementations26 Jul 2021 Liang Zeng, Lei Wang, Hui Niu, Ruchen Zhang, Ling Wang, Jian Li

In a set of experiments on three real-world financial markets: stocks, cryptocurrencies, and ETFs, LARA significantly outperforms several machine learning based methods on the Qlib quantitative investment platform.

Metric Learning Time Series Analysis

Effective Graph Learning with Adaptive Knowledge Exchange

no code implementations10 Jun 2021 Liang Zeng, Jin Xu, Zijun Yao, Yanqiao Zhu, Jian Li

Extensive experiments on node classification, graph classification, and edge prediction demonstrate the effectiveness of AKE-GNN.

Graph Classification Graph Learning +3

Context-Aware Sparse Deep Coordination Graphs

1 code implementation ICLR 2022 Tonghan Wang, Liang Zeng, Weijun Dong, Qianlan Yang, Yang Yu, Chongjie Zhang

Learning sparse coordination graphs adaptive to the coordination dynamics among agents is a long-standing problem in cooperative multi-agent learning.

graph construction Graph Learning +2

AnatomyNet: Deep Learning for Fast and Fully Automated Whole-volume Segmentation of Head and Neck Anatomy

2 code implementations15 Aug 2018 Wentao Zhu, Yufang Huang, Liang Zeng, Xuming Chen, Yong liu, Zhen Qian, Nan Du, Wei Fan, Xiaohui Xie

Methods: Our deep learning model, called AnatomyNet, segments OARs from head and neck CT images in an end-to-end fashion, receiving whole-volume HaN CT images as input and generating masks of all OARs of interest in one shot.

3D Medical Imaging Segmentation Anatomy

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